The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively.

The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.

Abstract: Due to the latest technological advances, the current society has the possibility to store large volumes of data in the majority of the problems of the daily life. These data are useless if there is not a set of techniques available to analyze them with the objective of obtaining knowledge that facilitates the problem resolution. This paper focuses on the techniques provided by data mining as a tool for intelligent data analysis in the field of human activity recognition, specifically in the application of two techniques of data mining capable of carrying out the extraction of knowledge from data that…are not as accurate and exact as desirable. This type of data reflects the true nature of the information collected on a day-to-day basis. The proposed techniques allow performing a preprocessing of the data by means of an instance selection that improves the computational requirements of the system response, obtaining satisfactory accuracy results. Several experiments are carried out on a real world dataset and various datasets obtained from the previous one in a synthetic way to simulate more realistic datasets that illustrate the potential of the proposed techniques.
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Abstract: Intelligent Environments (IEs) are physical spaces where Information Technology (IT) and other pervasive computing technologies are combined in order to achieve specific goals for the users and the environment. IEs have the goal of enriching user experience, increasing awareness of the environment. A number of applications are currently being deployed in domains ranging from smart homes to e-health and autonomous vehicles. Quite often IE support human activities, thus essential requirements to be ensured are correctness, reliability, safety and security. In this paper we present how a set of techniques and tools that have been developed for the verification of software…can be employed in the verification of IE described by means of event-condition-action rules. More precisely, we reduce the problem of verifying key properties of these rules to satisfiability and termination problems that can be addressed using state-of-the-art Satisfiability Modulo Theory (SMT) solvers and program analysers. Our approach has been implemented in a tool called vIRONy. Our approach has been validated on a number of case studies from the literature.
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Abstract: Many of public authorities are acknowledging the opportunity to invest into the development of intelligent infrastructures in order to offer a wide spectrum of smart services to their citizens. The development of such services supports a two-fold purpose: citizens enjoy high quality services with the minimum cost for the public authorities. The transition from traditional cities to smart cities that offer smart services to their citizens benefits various aspects of human activities like cultural heritage. Efficient management of urban cultural heritage should play a vital role within a smart city strategy. Carnivals are a popular manifestation of urban cultural heritage…that attracts large crowds of people. The successful organization of a carnival and especially its most significant event, the carnival parade, would be more beneficial if an intelligent environment could be established to guide the effort. In this work, we demonstrate a system that addresses to public authorities’ officials needs concerning the efficient planning and management of carnival parades. The proposed system design uses a combination of technologies from dedicated mobile applications and easy-to-use online interfaces to complex estimation algorithms that analyze collected data in real-time. The system provides accurate suggestions to event managers in order to help them make sound decisions on-the-fly during the parade (crowd management in terms of safety, spectator perceived usefulness and entertainment) or critical planning decisions prior the carnival parade (cost effectiveness concerning personnel management). We demonstrate system strength through a series of routine scenarios that occur when planning and conducting a carnival parade and we perform a three-level evaluation process to assess system services from various aspects.
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Abstract: The numerical value discretization is a process that is performed in the data preprocessing phase of intelligent data analysis. Preprocessing phase is very relevant because the quality of the models obtained in data mining step depends on this phase. Value discretization is an important task in data preprocessing because not all data mining techniques can handle continuous values. In this paper an unsupervised technique to discretize continuous data values using fuzzy partitions is proposed. Specifically a clustering technique that gets fuzzy partitions is presented. In addition, to evaluate the behavior of the proposed technique a series of experiments have been…proposed using a Extreme Learning Machine classifier and a committee of Extreme Learning Machine. Beside comparing with the K-means discretization technique. These experiments have been validated statistically obtaining the best results the approach proposed.
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